Trees1 Make Money Fast! Stock Fraud Ponzi Scheme Bank Robbery © 2013 Goodrich, Tamassia, Goldwasser

Trees2 What is a Tree  In computer science, a tree is an abstract model of a hierarchical structure  A tree consists of nodes with a parent-child relation  Applications: Organization charts File systems Programming environments Computers”R”Us SalesR&DManufacturing LaptopsDesktops US International EuropeAsiaCanada © 2013 Goodrich, Tamassia, Goldwasser

Trees3 subtree Tree Terminology  Root: node without parent (A)  Internal node: node with at least one child (A, B, C, F)  External node (a.k.a. leaf ): node without children (E, I, J, K, G, H, D)  Ancestors of a node: parent, grandparent, grand-grandparent, etc.  Depth of a node: number of ancestors  Height of a tree: maximum depth of any node (3)  Descendant of a node: child, grandchild, grand-grandchild, etc. A B DC GH E F IJ K  Subtree: tree consisting of a node and its descendants © 2013 Goodrich, Tamassia, Goldwasser

Trees4 Tree ADT  We use positions to abstract nodes  Generic methods: Integer len() Boolean is_empty() Iterator positions() Iterator iter()  Accessor methods: position root() position parent(p) Iterator children(p) Integer num_children(p) Query methods: Boolean is_leaf(p) Boolean is_root(p) Update method: element replace (p, o) Additional update methods may be defined by data structures implementing the Tree ADT © 2013 Goodrich, Tamassia, Goldwasser

Abstract Tree Class in Python © 2013 Goodrich, Tamassia, GoldwasserTrees5

6 Preorder Traversal  A traversal visits the nodes of a tree in a systematic manner  In a preorder traversal, a node is visited before its descendants  Application: print a structured document Make Money Fast! 1. MotivationsReferences2. Methods 2.1 Stock Fraud 2.2 Ponzi Scheme 1.1 Greed1.2 Avidity 2.3 Bank Robbery Algorithm preOrder(v) visit(v) for each child w of v preorder (w) © 2013 Goodrich, Tamassia, Goldwasser

Trees7 Postorder Traversal  In a postorder traversal, a node is visited after its descendants  Application: compute space used by files in a directory and its subdirectories Algorithm postOrder(v) for each child w of v postOrder (w) visit(v) cs16/ homeworks/ todo.txt 1K programs/ DDR.java 10K Stocks.java 25K h1c.doc 3K h1nc.doc 2K Robot.java 20K © 2013 Goodrich, Tamassia, Goldwasser

Trees8 Binary Trees  A binary tree is a tree with the following properties: Each internal node has at most two children (exactly two for proper binary trees) The children of a node are an ordered pair  We call the children of an internal node left child and right child  Alternative recursive definition: a binary tree is either a tree consisting of a single node, or a tree whose root has an ordered pair of children, each of which is a binary tree  Applications: arithmetic expressions decision processes searching A B C FG D E H I © 2013 Goodrich, Tamassia, Goldwasser

Trees9 Arithmetic Expression Tree  Binary tree associated with an arithmetic expression internal nodes: operators external nodes: operands  Example: arithmetic expression tree for the expression (2  ( a  1)  (3  b))    2 a1 3b © 2013 Goodrich, Tamassia, Goldwasser

Trees10 Decision Tree  Binary tree associated with a decision process internal nodes: questions with yes/no answer external nodes: decisions  Example: dining decision Want a fast meal? How about coffee?On expense account? StarbucksSpike’sAl FornoCafé Paragon Yes No YesNoYesNo © 2013 Goodrich, Tamassia, Goldwasser

Trees11 Properties of Proper Binary Trees  Notation n number of nodes e number of external nodes i number of internal nodes h height Properties: e  i  1 n  2e  1 h  i h  (n  1)  2 e  2 h h  log 2 e h  log 2 (n  1)  1 © 2013 Goodrich, Tamassia, Goldwasser

Trees12 BinaryTree ADT  The BinaryTree ADT extends the Tree ADT, i.e., it inherits all the methods of the Tree ADT  Additional methods: position left(p) position right(p) position sibling(p)  Update methods may be defined by data structures implementing the BinaryTree ADT © 2013 Goodrich, Tamassia, Goldwasser

Trees13 Inorder Traversal  In an inorder traversal a node is visited after its left subtree and before its right subtree  Application: draw a binary tree x(v) = inorder rank of v y(v) = depth of v Algorithm inOrder(v) if v has a left child inOrder (left (v)) visit(v) if v has a right child inOrder (right (v)) © 2013 Goodrich, Tamassia, Goldwasser

Trees14 Print Arithmetic Expressions  Specialization of an inorder traversal print operand or operator when visiting node print “(“ before traversing left subtree print “)“ after traversing right subtree Algorithm printExpression(v) if v has a left child print( “(’’ ) inOrder (left(v)) print(v.element ()) if v has a right child inOrder (right(v)) print ( “)’’ )    2 a1 3b ((2  ( a  1))  (3  b)) © 2013 Goodrich, Tamassia, Goldwasser

Trees15 Evaluate Arithmetic Expressions  Specialization of a postorder traversal recursive method returning the value of a subtree when visiting an internal node, combine the values of the subtrees Algorithm evalExpr(v) if is_leaf (v) return v.element () else x  evalExpr(left (v)) y  evalExpr(right (v))   operator stored at v return x  y    © 2013 Goodrich, Tamassia, Goldwasser

Trees16 Euler Tour Traversal  Generic traversal of a binary tree  Includes a special cases the preorder, postorder and inorder traversals  Walk around the tree and visit each node three times: on the left (preorder) from below (inorder) on the right (postorder)    L B R  © 2013 Goodrich, Tamassia, Goldwasser

Trees17  Linked Structure for Trees  A node is represented by an object storing Element Parent node Sequence of children nodes  Node objects implement the Position ADT B D A CE F B  ADF  C  E © 2013 Goodrich, Tamassia, Goldwasser

Trees18 Linked Structure for Binary Trees  A node is represented by an object storing Element Parent node Left child node Right child node  Node objects implement the Position ADT B D A CE   BADCE  © 2013 Goodrich, Tamassia, Goldwasser

Array-Based Representation of Binary Trees  Nodes are stored in an array A © 2013 Goodrich, Tamassia, Goldwasser19Trees  Node v is stored at A[rank(v)] rank(root) = 1 if node is the left child of parent(node), rank(node) = 2  rank(parent(node)) if node is the right child of parent(node), rank(node) = 2  rank(parent(node))  A HG FE D C B J ABDGH … …